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The Development of Causal Categorization
Author(s) -
Hayes Brett K.,
Rehder Bob
Publication year - 2012
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2012.01244.x
Subject(s) - categorization , centrality , psychology , causal model , coherence (philosophical gambling strategy) , logistic regression , generative grammar , cognitive psychology , causality (physics) , causal inference , cognition , probabilistic logic , artificial intelligence , developmental psychology , computer science , machine learning , mathematics , statistics , physics , quantum mechanics , neuroscience
Two experiments examined the impact of causal relations between features on categorization in 5‐ to 6‐year‐old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence , causal status , and relational centrality . Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1) but showed additional influences of causal status when links were probabilistic (Experiment 2). Children’s classification was based primarily on causal coherence in both cases. There was no effect of relational centrality in either age group. These results suggest that the generative model (Rehder, 2003a) provides a good account of causal categorization in children as well as adults.